A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Mathematical Problems in Engineering

Volumn 2015, Issue , 2015, Pages

  Zhang, Yudong ^a   Wang, Shuihua ^a,b   Ji, Genlin ^a  

^a NANJING NORMAL UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ANT COLONY OPTIMIZATION; BIOLOGY; COMPUTATION THEORY; CONSTRAINED OPTIMIZATION; DISTRIBUTED COMPUTER SYSTEMS; GENETIC ALGORITHMS; GLOBAL OPTIMIZATION; HEURISTIC ALGORITHMS; HEURISTIC METHODS; INFORMATION THEORY; OPERATIONS RESEARCH; SIMULATED ANNEALING; SURVEYS; TABU SEARCH;

ARTIFICIAL BEE COLONIES; ARTIFICIAL IMMUNE SYSTEM; AUTOMATION CONTROL SYSTEM; BIOGEOGRAPHY-BASED OPTIMIZATIONS; ELECTRICAL AND ELECTRONIC ENGINEERINGS; HEURISTIC GLOBAL OPTIMIZATION; PARALLEL IMPLEMENTATIONS; PARTICLE SWARM OPTIMIZATION ALGORITHM;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84945291666     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2015/931256     Document Type: Review

References (349)

1. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, New York, NY, USA, 2010.
2. B. L. Agarwal, Basic Statistics, NewAge International, 2006.
3. K. E. Voges and N. Pope, Business Applications and Computational Intelligence, Idea Group Publishing, 2006.
4. V. Kothari, J. Anuradha, S. Shah, andP.Mittal, "Asurvey onparticle swarm optimization in feature selection," in Global Trends in Information Systems and Software Applications: Proceedings of the 4th International Conference, ObCom 2011, Vellore, TN, India,December 9-11, 2011, Part 2, P. V. Krishna,M. R. Babu, and E.Ariwa, Eds., vol. 270, pp. 192-201, Springer, Berlin,Germany, 2012.
5. M. Dorigo, Optimization, learning and natural algorithms [Ph.D. thesis], Politecnico di Milano,Milano, Italy, 1992.
6. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948,December 1995.
7. R. Storn and K. Price, "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces," Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, 1997.
8. S.Müller, S. Airaghi, J.Marchetto, and P. Koumoutsakos, "Optimization algorithms based on amodel of bacterial chemotaxis," in Proceedings of the 6th International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 375-384, 2000.
9. K.M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control," IEEE Control SystemsMagazine, vol. 22, no. 3, pp. 52-67, 2002.
10. D. Karaboga and B. Basturk, "Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems," in Proceedings of the Foundations of Fuzzy Logic and Soft Computing, P. Melin, O. Castillo, L. T. Aguilar, and W. Pedrycz, Eds., pp. 789-798, Springer, New York, NY, USA, 2007.
11. K.N. Krishnanand andD. Ghose, "Detection ofmultiple source locations using a glowworm metaphor with applications to collective robotics," in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '05), pp. 84-94, June 2005.
12. X. S. Yang, "A new metaheuristic bat-inspired algorithm," in Nicso 2010: Nature Inspired Cooperative Strategies, J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., pp. 65-74, Springer, Berlin, Germany, 2010.
13. Y. Zhang, P. Agarwal, V. Bhatnagar, S. Balochian, andX.Zhang, "Swarm intelligence and its applications 2014," The Scientific World Journal, vol. 2014, Article ID 204294, 4 pages, 2014.
14. E. Bonabeau, M. Dorigo, and G.Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York, NY, USA, 1999.
15. M. Millonas, "Swarms, phase transitions and collective intelligence," in Artificial life III, C. Langton, Ed., pp. 417-445, Addison-Wesley, Reading, Mass, USA, 1994.
16. Y. Zhang, S. Balochian, P. Agarwal, V. Bhatnagar, and O. J. Housheya, "Artificial intelligence and its applications," Mathematical Problems in Engineering, vol. 2014, Article ID 840491, 10 pages, 2014.
17. F. Shahzad, S. Masood, and N. K. Khan, "Probabilistic opposition-based particle swarm optimization with velocity clamping," Knowledge and Information Systems, vol. 39, no. 3, pp. 703-737, 2014.
18. M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002.
19. Y. Zhang, S. Wang, and Z. Dong, "Classification of alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree," Progress in Electromagnetics Research, vol. 144, pp. 171-184, 2014.
20. Y.-M. Jau, K.-L. Su, C.-J.Wu, and J.-T. Jeng, "Modified quantumbehaved particle swarm optimization for parameters estimation of generalized nonlinear multi-regressions model based on Choquet integral with outliers," Applied Mathematics and Computation, vol. 221, pp. 282-295, 2013.
21. M. Jamalipour, R. Sayareh,M. Gharib, F. Khoshahval, and M. R. Karimi, "Quantum behaved particle swarm optimization with differential mutation operator applied to WWER-1000 in-core fuel management optimization," Annals of Nuclear Energy, vol. 54, pp. 134-140, 2013.
22. A. Bagheri, H.Mohammadi Peyhani, and M. Akbari, "Financial forecasting using ANFIS networkswithQuantum-behaved Particle Swarm Optimization," Expert Systems with Applications, vol. 41, no. 14, pp. 6235-6250, 2014.
23. D.Y.Tang, Y. Cai, J. Zhao, andY. Xue, "Aquantum-behavedparticle swarmoptimization withmemetic algorithmand memory for continuous non-linear large scale problems," Information Sciences, vol. 289, pp. 162-189, 2014.
24. E. Davoodi, M. T. Hagh, and S. G. Zadeh, "A hybrid improved quantum-behaved particle swarm optimizationsimplex method (IQPSOS) to solve power system load flow problems," Applied Soft Computing Journal, vol. 21, pp. 171-179, 2014.
25. P. Li and H. Xiao, "An improved quantum-behaved particle swarmoptimization algorithm," Applied Intelligence, vol. 40,no. 3, pp. 479-496, 2014.
26. D. Yumin and Z. Li, "Quantum behaved particle swarm optimization algorithm based on artificial fish swarm," Mathematical Problems in Engineering, vol. 2014, Article ID 592682, 10 pages, 2014.
27. P. F. Jia, F. C. Tian, S. Fan, Q. H. He, J.W. Feng, and S. X. Yang, "A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization," Sensor Review, vol. 34, no. 3, pp. 304-311, 2014.
28. S. Gholizadeh and R. K. Moghadas, "Performance-based optimum design of steel frames by an improved quantum particle swarm optimization," Advances in Structural Engineering, vol. 17, no. 2, pp. 143-156, 2014.
29. J. Kennedy, "Bare bones particle swarms," in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '03), pp. 80-87, Indianapolis, Ind, USA, 2003.
30. H. Zhang, D. D. Kennedy, G. P. Rangaiah, and A. Bonilla-Petriciolet, "Novel bare-bones particle swarmoptimization and its performance for modeling vapor-liquid equilibrium data," Fluid Phase Equilibria, vol. 301, no. 1, pp. 33-45, 2011.
31. J. Q. Zhang, L. Ni, J. Yao, W. Wang, and Z. Tang, "Adaptive bare bones particle swarm inspired by cloud model," IEICE Transactions on Information and Systems, vol. 94,no. 8, pp. 1527-1538, 2011.
32. H. Zhang, J. A. Fernández-Vargas, G. P. Rangaiah, A. Bonilla-Petriciolet, and J. G. Segovia-Hernández, "Evaluation of integrated differential evolution and unified bare-bones particle swarm optimization for phase equilibrium and stability problems," Fluid Phase Equilibria, vol. 310, no. 1-2, pp. 129-141, 2011.
33. Y. Zhang, D.-W. Gong, and Z. Ding, "A bare-bones multiobjective particle swarm optimization algorithm for environmental/economic dispatch," Information Sciences, vol. 192, pp. 213-227, 2012.
34. T. Blackwell, "A study of collapse in bare bones particle swarm optimization," IEEE Transactions on Evolutionary Computation, vol. 16, no. 3, pp. 354-372, 2012.
35. P. P. Wang, L. Shi, Y. Zhang, and L. Han, "A hybrid simplex search and modified bare-bones particle swarm optimization," Chinese Journal of Electronics, vol. 22, no. 1, pp. 104-108, 2013.
36. B. Jiang and N. Wang, "Cooperative bare-bone particle swarm optimization for data clustering," Soft Computing, vol. 18, no. 6, pp. 1079-1091, 2014.
37. H. Liu, G. Ding, and B. Wang, "Bare-bones particle swarm optimization with disruption operator," Applied Mathematics and Computation, vol. 238, pp. 106-122, 2014.
38. M. Campos, R. A. Krohling, and I. Enriquez, "Bare bones particle swarm optimization with scale matrix adaptation," IEEE Transactions on Cybernetics, vol. 44, no. 9, pp. 1567-1578, 2014.
39. Y. Zhang, D. Gong, Y. Hu, and W. Zhang, "Feature selection algorithm based on bare bones particle swarm optimization," Neurocomputing, vol. 148, pp. 150-157, 2015.
40. L.-Y. Chuang, S.-W. Tsai, and C.-H. Yang, "Chaotic catfish particle swarm optimization for solving global numerical optimization problems," Applied Mathematics and Computation, vol. 217, no. 16, pp. 6900-6916, 2011.
41. Y. Zhang and L. Wu, "Crop classification by forward neural network with adaptive chaotic particle swarm optimization," Sensors, vol. 11, no. 5, pp. 4721-4743, 2011.
42. Y. S.Dai,Y.Wei, J.Chen, Y. Zhang, andJ.Ding, "Seismicwavelet estimation based on adaptive chaotic embedded particle swarm optimization algorithm," in Proceedings of the 5th International Symposium on Computational Intelligence and Design (ISCID '12), vol. 2, pp. 57-60,October 2012.
43. C. S. Li, J. Zhou, P. Kou, and J. Xiao, "A novel chaotic particle swarm optimization based fuzzy clustering algorithm," Neurocomputing, vol. 83, pp. 98-109, 2012.
44. Q. Wu, R. Law, E. Wu, and J. Lin, "A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization," Information Sciences, vol. 238, pp. 96-110, 2013.
45. Y. Zhang, L. Wu, and S. Wang, "UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization," Mathematical Problems in Engineering, vol. 2013, Article ID 705238, 9 pages, 2013.
46. Q. Zhang, Z. Li, C. J. Zhou, and X. P. Wei, "Bayesian network structure learning based on the chaotic particle swarm optimization algorithm," Genetics and Molecular Research, vol. 12, no. 4, pp. 4468-4479, 2013.
47. C.-H. Yang, Y.-D. Lin, L.-Y. Chuang, and H.-W. Chang, "Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions," BioMed Research International, vol. 2014, Article ID 172049, 10 pages, 2014.
48. L. H. Son, "Optimizing municipal solid waste collection using chaotic particle swarm optimization in GIS based environments: a case study at Danang city, Vietnam," Expert Systems with Applications, vol. 41, no. 18, pp. 8062-8074, 2014.
49. H.-D. He, W.-Z. Lu, and Y. Xue, "Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm," Building and Environment, vol. 78, pp. 111-117, 2014.
50. Y. J. Zeng and Y. G. Sun, "An improved particle swarm optimization for the combined heat and power dynamic economic dispatch problem," Electric Power Components and Systems, vol. 42, no. 15, pp. 1700-1716, 2014.
51. M. Pluhacek, R. Senkerik, and I. Zelinka, "Particle swarm optimization algorithm driven by multichaotic number generator," Soft Computing, vol. 18, no. 4, pp. 631-639, 2014.
52. Y.-T. Juang, S.-L. Tung, and H.-C. Chiu, "Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions," Information Sciences, vol. 181, no. 20, pp. 4539-4549, 2011.
53. A. Alfi and M.-M. Fateh, "Intelligent identification and control using improved fuzzy particle swarm optimization," Expert Systems with Applications, vol. 38, no. 10, pp. 12312-12317, 2011.
54. W.-A. Yang, Y. Guo, and W.-H. Liao, "Optimization of multipass face milling using a fuzzy particle swarm optimization algorithm," International Journal of Advanced Manufacturing Technology, vol. 54, no. 1-4, pp. 45-57, 2011.
55. M. S. Norouzzadeh, M. R. Ahmadzadeh, and M. Palhang, "LADPSO: using fuzzy logic to conduct PSO algorithm," Applied Intelligence, vol. 37, no. 2, pp. 290-304, 2012.
56. A.Robati, G. A. Barani,H.NezamAbadiPour,M. J. Fadaee, and J. R. Anaraki, "Balanced fuzzy particle swarm optimization," Applied Mathematical Modelling, vol. 36, no. 5, pp. 2169-2177, 2012.
57. S. A. Khan and A. P. Engelbrecht, "A fuzzy particle swarm optimization algorithm for computer communication network topology design," Applied Intelligence, vol. 36, no. 1, pp. 161-177, 2012.
58. V. Galzina, R. Lujić, and T. Šarić, "Adaptive fuzzy particle swarm optimization for flow-shop scheduling problem," Tehnicki Vjesnik-Technical Gazette, vol. 19, no. 1, pp. 151-157, 2012.
59. M. Nafar, G. B. Gharehpetian, and T. Niknam, "Using modified fuzzy particle swarm optimization algorithm for parameter estimation of surge arresters models," International Journal of Innovative Computing, Information and Control, vol. 8, no. 1, pp. 567-581, 2012.
60. E. Aminian and M. Teshnehlab, "A novel fuzzy particle swarm optimization," in Proceedings of the 13th Iranian Conference on Fuzzy Systems, pp. 1-6, IEEE, Qazvin, Iran, 2013.
61. R. Chai, S. H. Ling, G. P. Hunter, Y. Tran, and H. T. Nguyen, "Brain-computer interface classifier forwheelchair commands using neural network with fuzzy particle swarm optimization," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 5, pp. 1614-1624, 2014.
62. X. J. Cai, Y. Cui, and Y. Tan, "Predictedmodified PSO with timevarying accelerator coefficients," International Journal of Bio-Inspired Computation, vol. 1, no. 1-2, pp. 50-60, 2009.
63. K. T. Chaturvedi, M. Pandit, and L. Srivastava, "Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch," International Journal of Electrical Power & Energy Systems, vol. 31, no. 6, pp. 249-257, 2009.
64. P. Boonyaritdachochai, C. Boonchuay, andW. Ongsakul, "Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients," Computers & Mathematics with Applications, vol. 60, no. 4, pp. 1068-1077, 2010.
65. C. Y. Sun, H. N. Zhao, and Y. F. Wang, "A comparative analysis of PSO, HPSO, and HPSO-TVAC for data clustering," Journal of Experimental andTheoretical Artificial Intelligence, vol. 23,no. 1, pp. 51-62, 2011.
66. O. Abedinia, N. Amjady, and K. Kiani, "Optimal complex economic load dispatch solution using particle swarmoptimization with time varying acceleration coefficient," International Review of Electrical Engineering, vol. 7, no. 2, pp. 4249-4256, 2012.
67. B. Mohammadi-Ivatloo, A. Rabiee, A. Soroudi, and M. Ehsan, "Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems," International Journal of Electrical Power & Energy Systems, vol. 42, no. 1, pp. 508-516, 2012.
68. B. Mohammadi-Ivatloo, M. Moradi-Dalvand, and A. Rabiee, "Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients," Electric Power Systems Research, vol. 95, pp. 9-18, 2013.
69. S. Pookpunt and W. Ongsakul, "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, vol. 55, pp. 266-276, 2013.
70. O. Abedinia, N. Amjady, A. Ghasemi, and Z. Hejrati, "Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques," International Transactions on Electrical Energy Systems, vol. 23, no. 8, pp. 1504-1522, 2013.
71. M. N. Abdullah, A. H. A. Bakar, N. A. Rahim, H. Mokhlis, H. A. Illias, and J. J. Jamian, "Modified particle swarm optimization with time varying acceleration coefficients for economic load dispatch with generator constraints," Journal of Electrical Engineering & Technology, vol. 9, no. 1, pp. 15-26, 2014.
72. M. C. Chih, C.-J. Lin, M.-S. Chern, and T.-Y. Ou, "Particle swarmoptimization with time-varying acceleration coefficients for the multidimensional knapsack problem," Applied Mathematical Modelling, vol. 38, no. 4, pp. 1338-1350, 2014.
73. H. Dhahri and A. M. Alimi, "Opposition-based particle swarm optimization for the design of beta basis function neural network," in Proceedings of the International Joint Conference on Neural Networks (IJCNN '10), pp. 1-8, 2010.
74. H. Wang, Z. Wu, S. Rahnamayan, Y. Liu, and M. Ventresca, "Enhancing particle swarm optimization using generalized opposition-based learning," Information Sciences, vol. 181, no. 20, pp. 4699-4714, 2011.
75. N. Dong, C.-H. Wu, W.-H. Ip, Z.-Q. Chen, C.-Y. Chan, and K.-L. Yung, "An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection," Computers & Mathematics with Applications, vol. 64, no. 6, pp. 1886-1902, 2012.
76. W. F. Gao, S. Y. Liu, and L. L. Huang, "Particle swarmoptimization with chaotic opposition-based population initialization and stochastic search technique," Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 11, pp. 4316-4327, 2012.
77. N. K. Khan, A. R. Baig, and M. A. Iqbal, "Opposition-based discrete PSO using natural encoding for classification rule discovery," International Journal of Advanced Robotic Systems, vol. 9, 2012.
78. M. Kaucic, "A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization," Journal of Global Optimization, vol. 55, no. 1, pp. 165-188, 2013.
79. Y. Dai, L. Liu, and S. Feng, "On the identification of coupled pitch and heavemotions using opposition-based particle swarm optimization,"Mathematical Problems in Engineering, vol. 2014, Article ID 784049, 10 pages, 2014.
80. D. M. Muñoz, C. H. Llanos, L. D. S. Coelho, and M. Ayala-Rincón, "Hardware opposition-based PSO applied to mobile robot controllers," Engineering Applications of Artificial Intelligence, vol. 28, pp. 64-77, 2014.
81. C. Guochu, "Simplified particle swarm optimization algorithm based on particles classification," in Proceedings of the 6th International Conference on Natural Computation (ICNC' 10), pp. 2701-2705, August 2010.
82. M. E. H. Pedersen and A. J. Chipperfield, "Simplifying particle swarm optimization," Applied Soft Computing Journal, vol. 10, no. 2, pp. 618-628, 2010.
83. C. H. Martins, R. P. B. dos Santos, and F. L. Santos, "Simplified particle swarm optimization algorithm," Acta Scientiarum-Technology, vol. 34, no. 1, pp. 21-25, 2012.
84. S. Panda, B. K. Sahu, and P. K. Mohanty, "Design and performance analysis of PID controller for an automatic voltage regulator systemusing simplified particle swarmoptimization," Journal of the Franklin Institute, vol. 349, no. 8, pp. 2609-2625, 2012.
85. N. K. Vastrakar and P. K. Padhy, "Simplified PSO PI-PD controller for unstable processes," in Proceedings of the 4th International Conference on Intelligent Systems, Modelling & Simulation (ISMS '13), pp. 350-354, IEEE, Bangkok, Thailand, January 2013.
86. W.-C. Yeh, "New parameter-free simplified swarmoptimization for artificial neural network training and its application in the prediction of time series," IEEE Transactions on Neural Networks and Learning Systems, vol. 24,no. 4, pp. 661-665, 2013.
87. S. Wang and J. M. Watada, "Two-stage fuzzy stochastic programming with Value-at-Risk criteria," Applied Soft Computing Journal, vol. 11, no. 1, pp. 1044-1056, 2011.
88. B. Jiang,N.Wang, and L. P.Wang, "Particle swarmoptimization with age-group topology for multimodal functions and data clustering," Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 11, pp. 3134-3145, 2013.
89. Y. Marinakis and M. Marinaki, "Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem," Soft Computing, vol. 17, no. 7, pp. 1159-1173, 2013.
90. J. Rada-Vilela, M. J. Zhang, andW. Seah, "A performance study on synchronicity and neighborhood size in particle swarm optimization," Soft Computing, vol. 17,no. 6, pp. 1019-1030, 2013.
91. H.Wang,H. Sun,C.Li, S. Rahnamayan, and J.-S.Pan, "Diversity enhanced particle swarm optimization with neighborhood search," Information Sciences, vol. 223, pp. 119-135, 2013.
92. X. Fu, W. Liu, B. Zhang, and H. Deng, "Quantum behaved particle swarm optimization with neighborhood search for numerical optimization," Mathematical Problems in Engineering, vol. 2013,Article ID 469723, 10 pages, 2013.
93. Q. J. Ni and J. M. Deng, "A new logistic dynamic particle swarmoptimization algorithm based on randomtopology," The Scientific World Journal, vol. 2013, Article ID 409167, 8 pages, 2013.
94. Z. Beheshti, S. M. Shamsuddin, and S. Sulaiman, "Fusion global-local-topology particle swarm optimization for global optimization problems,"Mathematical Problems in Engineering, vol. 2014, Article ID907386, 19 pages, 2014.
95. W. H. Lim and N. A. M. Isa, "Particle swarm optimization with increasing topology connectivity," Engineering Applications of Artificial Intelligence, vol. 27, pp. 80-102, 2014.
96. C. B. Kalayci and S. M. Gupta, "A particle swarm optimization algorithm with neighborhood-based mutation for sequencedependent disassembly line balancing problem," International Journal of AdvancedManufacturing Technology, vol. 69, no. 1-4, pp. 197-209, 2013.
97. L.-Y. Chuang, S.-W. Tsai, and C.-H. Yang, "Catfish particle swarm optimization," in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '08), IEEE, St. Louis, Mo, USA, September 2008.
98. R. F. Shi and X. J. Liu, "A hybrid improved particle swarm optimization based on dynamic parameters control and metropolis accept rule strategy," in Proceedings of the 3rd International Conference on Genetic and Evolutionary Computing (WGEC '09), T. L. Huang, L. F. Li, andM. Zhao, Eds., pp. 649-653, IEEE Computer Society, Los Alamitos, Calif, USA, October 2009.
99. J. H. Zhang, Y. Wang, R. Wang, and G. Hou, "Bidding strategy based on adaptive particle swarm optimization for electricity market," in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA '10), pp. 3207-3210, IEEE, Jinan, China, July 2010.
100. A. Liu, Y. Yang, Q. Xing, H. Yao, Y. Zhang, and Z. Zhou, "Improved collaborative particle swarm algorithm for job shop scheduling optimization," Advanced Science Letters, vol. 4, no. 6-7, pp. 2180-2183, 2011.
101. Y. X. Shen, G. Y. Wang, and C. M. Tao, "Particle swarm optimizationwith novel processing strategy and its application," International Journal of Computational Intelligence Systems, vol. 4, no. 1, pp. 100-111, 2011.
102. L. Lin, Z. Ji, S. He, and Z. Zhu, "A crown jewel defense strategy based particle swarm optimization," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '12), June 2012.
103. S. M. Wang and J. Watada, "A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy randomuncertainty," Information Sciences, vol. 192, pp. 3-18, 2012.
104. Z. Li, Z. Tian, Y. Xie, R. Huang, and J. Tan, "A knowledgebased heuristic particle swarm optimization approach with the adjustment strategy for the weighted circle packing problem," Computers &Mathematics with Applications, vol. 66, no. 10, pp. 1758-1769, 2013.
105. Y. C. Lu, J. C. Jan, S. L. Hung, and G. H. Hung, "Enhancing particle swarmoptimization algorithmusing two newstrategies for optimizing design of truss structures," Engineering Optimization, vol. 45, no. 10, pp. 1251-1271, 2013.
106. C. L. C. Mattos, G. A. Barreto, and F. R. P. Cavalcanti, "An improved hybrid particle swarm optimization algorithm applied to economic modeling of radio resource allocation," Electronic Commerce Research, vol. 14, no. 1, pp. 51-70, 2014.
107. G. Wu,W. Pedrycz,M.Ma, D. Qiu, H. Li, and J. Liu, "A particle swarm optimization variant with an inner variable learning strategy," The Scientific World Journal, vol. 2014, Article ID 713490, 15 pages, 2014.
108. W. H. Lim and N. A. Mat Isa, "An adaptive two-layer particle swarm optimization with elitist learning strategy," Information Sciences, vol. 273, pp. 49-72, 2014.
109. Y. Shimizu, T. Sakaguchi, and T.Miura, "Parallel computing for huge scale logistics optimization through binary PSO associated with topological comparison," Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol. 8, no. 1, Article ID JAMDSM0005, 2014.
110. I. Fister Jr., M. Perc, K. Ljubič, S. M. Kamal, and A. Iglesias, "Particle swarmoptimization for automatic creation of complex graphic characters," Chaos, Solitons & Fractals, vol. 73, pp. 29-35, 2015.
111. R. J. Kuo and C.W.Hong, "Integration of genetic algorithmand particle swarmoptimization for investment portfolio optimization," Applied Mathematics and Information Sciences, vol. 7, no. 6, pp. 2397-2408, 2013.
112. W. C. Chen and D. Kurniawan, "Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSOGA," International Journal of Precision Engineering and Manufacturing, vol. 15, no. 8, pp. 1583-1593, 2014.
113. M. Nazir, A. Majid-Mirza, and S. Ali-Khan, "PSO-GA based optimized feature selection using facial and clothing information for gender classification," Journal of Applied Research and Technology, vol. 12, no. 1, pp. 145-152, 2014.
114. K. Vidhya and K. R. S. Kumar, "Channel estimation of MIMOOFDM system using PSO and GA," Arabian Journal for Science and Engineering, vol. 39, no. 5, pp. 4047-4056, 2014.
115. Y. Xiao, J. Xiao, F. Lu, and S.Wang, "Ensemble ANNs-PSO-GA approach for day-ahead stock E-exchange prices forecasting," International Journal of Computational Intelligence Systems, vol. 7, no. 2, pp. 272-290, 2014.
116. P. Ghamisi and J. A. Benediktsson, "Feature selection based on hybridization of genetic algorithm and particle swarm optimization," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 309-313, 2015.
117. H. Z. Tang, Y. Xiao, H. Huang, and X. Guo, "A novel dynamic particle swarm optimization algorithm based on improved artificial immune network," in Proceedings of the 10th IEEE International Conference on Signal Processing (ICSP '10), B. Z. Yuan, Q. Q. Ruan, and X. F. Tang, Eds., pp. 103-106,New York, NY, USA, October 2010.
118. Y. Zhang, Y. Jun, G.Wei, and L.Wu, "Findmulti-objective paths in stochastic networks via chaoticimmune PSO," Expert Systems with Applications, vol. 37, no. 3, pp. 1911-1919, 2010.
119. A. A. Ibrahim, A. Mohamed, H. Shareef, and S. P. Ghoshal, "Optimal power quality monitor placement in power systems based on particle swarm optimization and artificial immune system," in Proceedings of the 3rd Conference on Data Mining and Optimization (DMO '11), pp. 141-145, June 2011.
120. R. J. Kuo, C.M. Chen,T.W.Liao, and F.C.Tien, "Hybrid of artificial immune system and particle swarm optimization-based support vector machine for radio frequency identificationbased positioning system," Computers and Industrial Engineering, vol. 64, no. 1, pp. 333-341, 2013.
121. Z.-H. Liu, J. Zhang, S.-W. Zhou, X.-H. Li, and K. Liu, "Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM," IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1921-1935, 2013.
122. S. Darzi, T. S. Kiong, M. T. Islam, M. Ismail, S. Kibria, and B. Salem, "Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization," The ScientificWorld Journal, vol. 2014,Article ID 724639, 10 pages, 2014.
123. Z. C. Li, D. J. Zheng, and H. J. Hou, "A hybrid particle swarm optimization algorithm based on nonlinear simplex method and tabu search," in Advances in Neural Networks-ISNN 2010, L. Q. Zhang, B. L. Lu, and J. Kwok, Eds., vol. 6063 of LectureNotes in Computer Science, pp. 126-135, Springer, Berlin, Germany, 2010.
124. S. Nakano, A. Ishigame, and K. Yasuda, "Consideration of particle swarmoptimization combined with tabu search," Electrical Engineering in Japan, vol. 172, no. 4, pp. 31-37, 2010.
125. T. Zhang, Y. J. Zhang, Q. P. Zheng, andP.M.Pardalos, "Ahybrid particle swarmoptimization and tabu seach algorithm for order planning problems of steel factories based on the make-tostock and make-to-order management architecture," Journal of Industrial andManagement Optimization, vol. 7, no. 1, pp. 31-51, 2011.
126. R.Ktari andH.Chabchoub, "Essential particle swarmoptimization queen with tabu search for MKP resolution," Computing, vol. 95, no. 9, pp. 897-921, 2013.
127. J. Wang, J. Lu, Z. Bie, S. You, and X. Cao, "Long-term maintenance scheduling of smart distribution system through a PSOTS algorithm," Journal of AppliedMathematics, vol. 2014,Article ID 694086, 12 pages, 2014.
128. S.-M. Chen and C.-Y. Chien, "Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques," Expert Systems with Applications, vol. 38, no. 12, pp. 14439-14450, 2011.
129. R. Xiao, W. Chen, and T. Chen, "Modeling of ant colony's labor division for the multi-project scheduling problem and its solution by pso," Journal of Computational and Theoretical Nanoscience, vol. 9, no. 2, pp. 223-232, 2012.
130. M. S. Kiran, E. Özceylan, M. Gündüz, and T. Paksoy, "A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey," Energy Conversion and Management, vol. 53, no. 1, pp. 75-83, 2012.
131. C.-L. Huang, W.-C. Huang, H.-Y. Chang, Y.-C. Yeh, and C.-Y. Tsai, "Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering," Applied Soft Computing Journal, vol. 13, no. 9, pp. 3864-3872, 2013.
132. R. Rahmani, R. Yusof, M. Seyedmahmoudian, and S. Mekhilef, "Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting," Journal of Wind Engineering and Industrial Aerodynamics, vol. 123, pp. 163-170, 2013.
133. W. Elloumi, N. Baklouti, A. Abraham, and A. M. Alimi, "The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization," Journal of Intelligent and Fuzzy Systems, vol. 27, no. 1, pp. 515-525, 2014.
134. S. M. Sait, A. T. Sheikh, and A.H. El-Maleh, "Cell assignment in hybridCMOS/nanodevices architecture using a PSO/SA hybrid algorithm," Journal of Applied Research and Technology, vol. 11, no. 5, pp. 653-664, 2013.
135. H. Jiang and L. Zou, "A hybrid PSO-SA optimizing approach for SVM models in classification," International Journal of Biomathematics, vol. 6, no. 5,Article ID 1350036, 18 pages, 2013.
136. T. Niknam,M. R. Narimani, andM. Jabbari, "Dynamic optimal power flow using hybrid particle swarm optimization and simulated annealing," International Transactions on Electrical Energy Systems, vol. 23, no. 7, pp. 975-1001, 2013.
137. F. Khoshahval, A. Zolfaghari, H. Minuchehr, and M. R. Abbasi, "A new hybridmethod formulti-objective fuelmanagement optimization using parallel PSO-SA," Progress in Nuclear Energy, vol. 76, pp. 112-121, 2014.
138. H. Du, P. H. Lou, and W. H. Ye, "Application of hybrid particle swarm optimization in resource constrained multiproject scheduling," Journal of the Chinese Society ofMechanical Engineers, vol. 35, no. 5, pp. 371-379, 2014.
139. Y. Zhang, S.Wang, Y. Sun, G. Ji, P. Phillips, and Z.Dong, "Binary structuring elements decomposition based on an improved recursive dilation-union model and RSAPSO method," Mathematical Problems in Engineering, vol. 2014, Article ID 272496, 12 pages, 2014.
140. J. Geng, M.-W. Li, Z.-H. Dong, and Y.-S. Liao, "Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm," Neurocomputing, vol. 147, pp. 239-250, 2015.
141. M. El-Abd, "Testing a particle swarmoptimization and artificial bee colony hybrid algorithm on the CEC13 benchmarks," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '13), pp. 2215-2220, IEEE, Cancún, Mexico, June 2013.
142. T. K. Sharma, M. Pant, and A. Abraham, "Blend of local and global variant of PSO in ABC," in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC '13), pp. 113-119, August 2013.
143. M. S. Kiran and M. Gündüz, "A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems," Applied Soft Computing Journal, vol. 13, no. 4, pp. 2188-2203, 2013.
144. L. N. Vitorino, S. F. Ribeiro, and C. J. A. Bastos-Filho, "A mechanism based on artificial bee colony to generate diversity in particle swarm optimization," Neurocomputing, vol. 148, pp. 39-45, 2015.
145. G. Maione and A. Punzi, "Combining differential evolution and particle swarm optimization to tune and realize fractionalorder controllers," Mathematical and Computer Modelling of Dynamical Systems, vol. 19, no. 3, pp. 277-299, 2013.
146. Y. G. Fu, M. Y. Ding, C. P. Zhou, and H. P. Hu, "Route planning for unmanned aerial vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, no. 6, pp. 1451-1465, 2013.
147. Vasundhara, D. Mandal, R. Kar, and S. P. Ghoshal, "Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization," Natural Computing, vol. 13, no. 1, pp. 55-64, 2014.
148. X. Yu, J. Cao, H. Shan, L. Zhu, and J. Guo, "An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization," The Scientific World Journal, vol. 2014,Article ID 215472, 16 pages, 2014.
149. G.-G. Wang, A. Hossein Gandomi, X.-S. Yang, and A. Hossein Alavi, "A novel improved accelerated particle swarm optimization algorithm for global numerical optimization," Engineering Computations, vol. 31, no. 7, pp. 1198-1220, 2014.
150. A. Yadav and K. Deep, "An efficient co-swarm particle swarm optimization for non-linear constrained optimization," Journal of Computational Science, vol. 5, no. 2, pp. 258-268, 2014.
151. W. Xu, Z. Geng, Q. Zhu, and X. Gu, "Optimal grade transition for polyethylene reactors based on simultaneous strategies and trust region particle swarm optimization," Industrial and Engineering Chemistry Research, vol. 52, no. 9, pp. 3363-3372, 2013.
152. S. R. Mohanty, N. Kishor, and P. K. Ray, "Robust H-infinite loop shaping controller based on hybrid PSO and harmonic search for frequency regulation in hybrid distributed generation system," International Journal of Electrical Power and Energy Systems, vol. 60, pp. 302-316, 2014.
153. W. Guo, W. Li, Q. Zhang, L. Wang, Q. Wu, and H. Ren, "Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems," Engineering Optimization, vol. 46, no. 11, pp. 1465-1484, 2014.
154. C. Y. Qiu, C. L. Wang, and X. Q. Zuo, "A novel multi-objective particle swarm optimization with K-means based global best selection strategy," International Journal of Computational Intelligence Systems, vol. 6, no. 5, pp. 822-835, 2013.
155. W.-J. Chen, W.-C. Su, and Y.-L. Yang, "Application of constrained multi-objective hybrid quantum particle swarm optimization for improving performance of an ironless permanent magnet linear motor," Applied Mathematics & Information Sciences, vol. 8, no. 6, pp. 3111-3120, 2014.
156. A. Ghanei, E. Assareh, M. Biglari, A. Ghanbarzadeh, and A. R. Noghrehabadi, "Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)," Heat andMass Transfer, vol. 50, no. 10, pp. 1375-1384, 2014.
157. C. Duan, X. Wang, S. Shu, C. Jing, and H. Chang, "Thermodynamic design of Stirling engine using multi-objective particle swarmoptimization algorithm," Energy Conversion and Management, vol. 84, pp. 88-96, 2014.
158. M. R. Amiryousefi, M. Mohebbi, F. Khodaiyan, and M. G. Ahsaee, "Multi-objective optimization of deep-fat frying of ostrich meat plates using multi-objective particle swarm optimization (MOPSO)," Journal of Food Processing and Preservation, vol. 38, no. 4, pp. 1472-1479, 2014.
159. S. Ganguly, "Multi-objective planning for reactive power compensation of radial distribution networks with unified power quality conditioner allocation using particle swarm optimization," IEEE Transactions on Power Systems, vol. 29, no. 4, pp. 1801-1810, 2014.
160. E. Z. Zhang, Y. F.Wu, andQ.W. Chen, "Apractical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization," Reliability Engineering and System Safety, vol. 127, pp. 65-76, 2014.
161. R. Perera, E. Sevillano, A. Arteaga, and A. de Diego, "Identification of intermediate debonding damage in FRP-plated RC beams based on multi-objective particle swarm optimization without updated baseline model," Composites Part B: Engineering, vol. 62, pp. 205-217, 2014.
162. S. Cheng, M.-Y. Chen, and P. J. Fleming, "Improved multiobjective particle swarm optimization with preference strategy for optimal DG integration into the distribution system," Neurocomputing, vol. 148, pp. 23-29, 2015.
163. M. Daneshyari and G. G. Yen, "Constrained multiple-swarm particle swarmoptimizationwithin a cultural framework," IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 42, no. 2, pp. 475-490, 2012.
164. M. H. Afshar, "Extension of the constrained particle swarm optimization algorithm to optimal operation of multireservoirs system," International Journal of Electrical Power & Energy Systems, vol. 51, pp. 71-81, 2013.
165. G. Koulinas, L. Kotsikas, and K. Anagnostopoulos, "A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem," Information Sciences, vol. 277, pp. 680-693, 2014.
166. J. J. Shan and Y. Ren, "Low-thrust trajectory design with constrained particle swarm optimization," Aerospace Science and Technology, vol. 36, pp. 114-124, 2014.
167. C. C. Yeh and Y. C. Chien, "A particle swarm optimization-like algorithm for constrained minimal spanning tree problems," Journal ofMarine Science and Technology, vol. 22, no. 3, pp. 341-351, 2014.
168. N. Singh, R. Arya, and R. K. Agrawal, "A novel approach to combine features for salient object detection using constrained particle swarmoptimization," Pattern Recognition, vol. 47, no. 4, pp. 1731-1739, 2014.
169. P. Paliwal, N. P. Patidar, and R. K. Nema, "Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization," Renewable Energy, vol. 63, pp. 194-204, 2014.
170. L. Z. Cui, Z. Ling, J.Poonet al., "Aparallelmodel of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization," Analytical Methods, vol. 6, no. 8, pp. 2679-2686, 2014.
171. Y. Y. Shou,Y.Li, andC.T.Lai, "Hybridparticle swarmoptimization for preemptive resource-constrained project scheduling," Neurocomputing, vol. 148, pp. 122-128, 2015.
172. M. Chen and S. A. Ludwig, "Discrete particle swarm optimization with local search strategy for rule classification," in Proceedings of the 4thWorld Congress on Nature and Biologically Inspired Computing (NaBIC '12), pp. 162-167, November 2012.
173. M. Shen, Z.-H. Zhan, W.-N. Chen, Y.-J. Gong, J. Zhang, and Y. Li, "Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks," IEEE Transactions on Industrial Electronics, vol. 61, no. 12, pp. 7141-7151, 2014.
174. C.-L. Chen, S.-Y. Huang, Y.-R. Tzeng, and C.-L. Chen, "A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem," Soft Computing, vol. 18, no. 11, pp. 2271-2282, 2014.
175. Q. Cai, M. Gong, B. Shen, L. Ma, and L. Jiao, "Discrete particle swarm optimization for identifying community structures in signed social networks," Neural Networks, vol. 58,pp. 4-13, 2014.
176. A. H. Kashan, B. Karimi, and A. Noktehdan, "A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem,"The International Journal of Advanced Manufacturing Technology, vol. 73, no. 9-12, pp. 1543-1556, 2014.
177. N. X. Xu, J. S.Gao, andX. G. Feng, "Study onthe optimal design of frequency selective surfaces based on the discrete particle swarmoptimization," Acta Physica Sinica, vol. 63, no. 13,Article ID 138401, 2014.
178. R. Garg and A. K. Singh, "Multi-objective workflow grid scheduling using ε-fuzzy dominance sort based discrete particle swarm optimization," Journal of Supercomputing, vol. 68, no. 2, pp. 709-732, 2013.
179. X. Zong, S. Xiong, and Z. Fang, "A conflict-congestion model for pedestrian-vehicle mixed evacuation based on discrete particle swarmoptimization algorithm," Computers andOperations Research, vol. 44, pp. 1-12, 2014.
180. R. Ezzeldin, B. Djebedjian, and T. Saafan, "Integer discrete particle swarm optimization of water distribution networks," Journal of Pipeline Systems Engineering and Practice, vol. 5, no. 1, article 154, 2014.
181. T. T. Zhai andZ. F.He, "Instance selection for time series classification based on immune binary particle swarmoptimization," Knowledge-Based Systems, vol. 49, pp. 106-115, 2013.
182. K. N. V. D. Sarath and V. Ravi, "Association rule mining using binary particle swarm optimization," Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1832-1840, 2013.
183. M. A. Taha and D. I. Abu Al Nadi, "Spectrum sensing for cognitive radio using binary particle swarm optimization," Wireless Personal Communications, vol. 72, no. 4, pp. 2143-2153, 2013.
184. A. H. El-Maleh, A. T. Sheikh, and S. M. Sait, "Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits," Applied Soft Computing Journal, vol. 13, no. 12, pp. 4832-4840, 2013.
185. A. Erturk, M. K. Gullu, D. Cesmeci, D. Gercek, and S. Erturk, "Spatial resolution enhancement of hyperspectral images using unmixing and binary particle swarm optimization," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 12, pp. 2100-2104, 2014.
186. Y. Zhang, S. Wang, P. Phillips, and G. Ji, "Binary PSO with mutation operator for feature selection using decision tree applied to spam detection," Knowledge-Based Systems, vol. 64, pp. 22-31, 2014.
187. H. T. Yin, P. Fu, and Z. Sun, "Face feature selection and recognition using separability criterion and binary particle swarm optimization algorithm," Chinese Journal of Electronics, vol. 23, no. 2, pp. 361-365, 2014.
188. J. Yang, H. Zhang, Y. Ling, C. Pan, andW. Sun, "Task allocation for wireless sensor network using modified binary particle swarm optimization," IEEE Sensors Journal, vol. 14, no. 3, pp. 882-892, 2014.
189. M. R. Ganesh, R. Krishna, K. Manikantan, and S. Ramachandran, "Entropy based Binary Particle Swarm Optimization and classification for ear detection," Engineering Applications of Artificial Intelligence, vol. 27, pp. 115-128, 2014.
190. A. R. Jordehi and J. Jasni, "Parameter selection in particle swarm optimisation: a survey," Journal of Experimental andTheoretical Artificial Intelligence, vol. 25, no. 4, pp. 527-542, 2013.
191. S. Kumar and D. K. Chaturvedi, "Tuning of particle swarm optimization parameter using fuzzy logic," in Proceedings of the International Conference on Communication Systems and Network Technologies (CSNT '11), pp. 174-179, June 2011.
192. W. Zhang, Y. Jin, X. Li, and X. Zhang, "A simple way for parameter selection of standard particle swarm optimization," in Artificial Intelligence and Computational Intelligence Part III, H. Deng, D. Miao, J. Lei, and F. L. Wang, Eds., pp. 436-443, Springer, Berlin, Germany, 2011.
193. H. Yang, "Particle swarm optimization with modified velocity strategy," in Proceedings of the International Conference on Energy and Environmental Science (ICEES '11), X. Zhou, Ed., pp. 1074-1079, Elsevier Science, 2011.
194. J. Sun, W. Fang, X. Wu, V. Palade, and W. Xu, "Quantumbehaved particle swarm optimization: analysis of individual particle behavior and parameter selection," Evolutionary Computation, vol. 20, no. 3, pp. 349-393, 2012.
195. I. M. Yassin, M. N. Taib, R. Adnan, M. K. M. Salleh, and M. K. Hamzah, "Effect of swarm size parameter on Binary Particle Swarm optimization-based NARX structure selection," in Proceedings of the IEEE Symposium on Industrial Electronics and Applications (ISIEA '12), pp. 219-223, Bandung City West Java, Indonesia, September 2012.
196. D.-S.Wang, H.-J.Wang, and J.-K. Zhang, "Selection of the PSO parameters for inverting of ellipsometry," in Proceedings of the International Conference on Industrial Control and Electronics Engineering (ICICEE 2012), pp. 776-780, August 2012.
197. H.Hao,N.Hu,X.Xu, andW.Q.Ying, "Parameters selectionand optimization of particle swarm optimization algorithm based on molecular force model," in Measurement Technology and Engineering Researches in Industry, P. Yarlagadda and Y. H. Kim, Eds., Parts 1-3, pp. 1370-1373, Trans Tech Publications, Stafa, Switzerland, 2013.
198. G. Xu, "An adaptive parameter tuning of particle swarm optimization algorithm," Applied Mathematics and Computation, vol. 219, no. 9, pp. 4560-4569, 2013.
199. P. Chauhan, K. Deep, andM. Pant, "Novel inertia weight strategies for particle swarm optimization," Memetic Computing, vol. 5, no. 3, pp. 229-251, 2013.
200. W. Zhang, D. Ma, J.-J. Wei, and H.-F. Liang, "A parameter selection strategy for particle swarm optimization based on particle positions," Expert Systems with Applications, vol. 41, no. 7, pp. 3576-3584, 2014.
201. M. Kanemasa and E. Aiyoshi, "Algorithm tuners for PSO methods and genetic programming techniques for learning tuning rules," IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 4, pp. 407-414, 2014.
202. K. Wang and J. H. Shen, "The convergence basis of particle swarmoptimization," in Proceedings of the International Conference on Industrial Control and Electronics Engineering (ICICEE '12), pp. 63-66,August 2012.
203. J. Sun, X. Wu, V. Palade, W. Fang, C.-H. Lai, and W. Xu, "Convergence analysis and improvements of quantum-behaved particle swarmoptimization," Information Sciences, vol. 193, pp. 81-103, 2012.
204. T. Kurihara and K. Jin'no, "Analysis of convergence property of PSO and its application to nonlinear blind source separation," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '13), pp. 976-981, IEEE, Cancun, Mexico, June 2013.
205. C.-C. Lin, "Dynamic router node placement in wireless mesh networks: a PSO approach with constriction coefficient and its convergence analysis," Information Sciences, vol. 232, pp. 294-308, 2013.
206. Y. Zhang, D.-W. Gong, X.-Y. Sun, and N. Geng, "Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis," Soft Computing, vol. 18, no. 7, pp. 1337-1352, 2014.
207. W. T. Lin, Z. Lian, X. Gu, and B. Jiao, "A local and global search combined particle swarmoptimization algorithm and its convergence analysis," Mathematical Problems in Engineering, vol. 2014, Article ID905712, 11 pages, 2014.
208. S. Kim and L. Li, "Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization," Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 413-432, 2014.
209. M. Waintraub, R. Schirru, and C. M. N. A. Pereira, "Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems," Progress in Nuclear Energy, vol. 51, no. 6, pp. 680-688, 2009.
210. S.-C. Yu, "Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization," Applied Soft Computing Journal, vol. 21, pp. 578-589, 2014.
211. Y. K. Hung and W. C. Wang, "Accelerating parallel particle swarm optimization via GPU," Optimization Methods and Software, vol. 27, no. 1, pp. 33-51, 2012.
212. B. Rymut, B. Kwolek, and T. Krzeszowski, "GPU-accelerated human motion tracking using particle filter combined with PSO," in Advanced Concepts for Intelligent Vision Systems, ACIVS 2013, J. Blanc-Talon, A. Kasinski,W.Philips,D.Popescu, and P. Scheunders, Eds., pp. 426-437, Springer, Berlin, Germany, 2013.
213. J. Kumar, L. Singh, and S.Paul, "GPUbased parallel cooperative particle swarm optimization using C-CUDA: a case study," in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '13), July 2013.
214. O. Awwad, A. Al-Fuqaha, G. Ben Brahim, B. Khan, and A. Rayes, "Distributed topology control in large-scale hybrid RF/FSO networks: SIMT GPU-based particle swarm optimization approach," International Journal of Communication Systems, vol. 26, no. 7, pp. 888-911, 2013.
215. R.-B. Chen, Y.-W. Hsu, Y. Hung, and W. Wang, "Discrete particle swarm optimization for constructing uniform design on irregular regions," Computational Statistics & Data Analysis, vol. 72, pp. 282-297, 2014.
216. J. Liu, X. G. Luo, andX.M. F.Zhang, "Job scheduling algorithm for cloud computing based on particle swarm optimization," in Nanotechnology and Precision Engineering, Pts 1 and 2, Z. Y. Jiang and Y. H. Kim, Eds., pp. 957-960, Trans Tech Publications, Stäfa, Switzerland, 2013.
217. Y. J. Xu and T. You, "Minimizing thermal residual stresses in ceramic matrix composites by using Iterative Map Reduce guided particle swarm optimization algorithm," Composite Structures, vol. 99, pp. 388-396, 2013.
218. F. Ramezani, J. Lu, and F. Hussain, "Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization," in Service-Oriented Computing, S. Basu, C.Pautasso, L. Zhang, andX. Fu, Eds., vol. 8274 of Lecture Notes in Computer Science, pp. 237-251, Springer, Berlin, Germany, 2013.
219. K. Govindarajan, T. S. Somasundaram, and V. S. Kumar, "Particle swarm optimization (PSO)-based clustering for improving the quality of learning using cloud computing," in Proceedings of the IEEE 13th International Conference on Advanced Learning Technologies (ICALT '13), pp. 495-497, IEEE, Beijing, China, July 2013.
220. F. Ramezani, J. Lu, and F. K. Hussain, "Task-based system load balancing in cloud computing using particle swarm optimization," International Journal of Parallel Programming, vol. 42, no. 5, pp. 739-754, 2014.
221. S. Ganguly, N. C. Sahoo, and D. Das, "Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation," Fuzzy Sets and Systems, vol. 213, pp. 47-73, 2013.
222. S. Komsiyah, "Computational methods of Gaussian particle swarm optimization (GPSO) and lagrange multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)," in Proceedings of the International Conference on Advances Science and Contemporary Engineering (ICASCE '13), vol. 68, 2014.
223. J. W. Feng, F.Tian, P. Jia, Q. He,Y. Shen, and S. Fan, "Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization," Sensor Review, vol. 34, no. 4, pp. 389-395, 2014.
224. E. Pekşen, T. Yas, and A. Kiyak, "1-D DC resistivity modeling and interpretation in anisotropic media using particle swarm optimization," Pure and Applied Geophysics, vol. 171, no. 9, pp. 2371-2389, 2014.
225. J. Yang, L. F. He, and S. Y. Fu, "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, vol. 128, pp. 82-92, 2014.
226. I. M. deMendonça, I. C. S. Junior, and A. L.M.Marcato, "Static planning of the expansion of electrical energy transmission systems using particle swarm optimization," International Journal of Electrical Power and Energy Systems, vol. 60, pp. 234-244, 2014.
227. T.-D. Liu, T.-E. Fan, G.-F. Shao, J.-W. Zheng, and Y.-H. Wen, "Particle swarm optimization of the stable structure of tetrahexahedral Pt-based bimetallic nanoparticles," Physics Letters A, vol. 378, no. 40, pp. 2965-2972, 2014.
228. U. Aich and S. Banerjee, "Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization," Applied Mathematical Modelling, vol. 38, no. 11-12, pp. 2800-2818, 2014.
229. C.-J. Chou, C.-Y. Lee, and C.-C. Chen, "Survey of reservoir grounding system defects considering the performance of lightning protection and improved design based on soil drilling data and the particle swarm optimization technique," IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 6, pp. 605-613, 2014.
230. B. A. Lee, B. S. Kim, M. S. Ko, K. Y. Kim, and S. Kim, "Electrical resistance imaging of two-phase flow with a mesh grouping technique based on particle swarm optimization," Nuclear Engineering and Technology, vol. 46, no. 1, pp. 109-116, 2014.
231. P. Thakral and A. K. Bakhshi, "Computational atomistic blueprinting of novel conducting copolymers using particle swarm optimization," Journal of Computer-Aided Molecular Design, vol. 28, no. 2, pp. 111-122, 2014.
232. I. Fister, X.-S. Yang, K. Ljubič, D. Fister, and J. Brest, "Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL," The ScientificWorld Journal, vol. 2014,Article ID 121782, 10 pages, 2014.
233. J. Aghaei, K. M. Muttaqi, A. Azizivahed, and M. Gitizadeh, "Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm," Energy, vol. 65, pp. 398-411, 2014.
234. A. Selakov, D. Cvijetinović, L.Milović, S.Mellon, and D. Bekut, "Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank," Applied Soft Computing Journal, vol. 16, pp. 80-88, 2014.
235. Y. Shirvany, Q. Mahmood, F. Edelvik, S. Jakobsson, A. Hedstrom, andM. Persson, "Particle swarmoptimization applied to EEG source localization of somatosensory evoked potentials," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 1, pp. 11-20, 2014.
236. D. H. Tungadio, B. P. Numbi, M. W. Siti, and A. A. Jimoh, "Particle swarm optimization for power system state estimation," Neurocomputing, vol. 148, pp. 175-180, 2015.
237. Y. F. Cai andS. X. Yang, "An improvedPSO-basedapproachwith dynamic parameter tuning for cooperative multi-robot target searching in complex unknown environments," International Journal of Control, vol. 86, no. 10, pp. 1720-1732, 2013.
238. K. Kolomvatsos and S. Hadjieftymiades, "On the use of particle swarmoptimization andKernel density estimator in concurrent negotiations," Information Sciences, vol. 262, pp. 99-116, 2014.
239. S. K. Pandey, S. R. Mohanty, N. Kishor, and J. P. S. Catalão, "Frequency regulation in hybrid power systems using particle swarmoptimization and linear matrix inequalities based robust controller design," International Journal of Electrical Power & Energy Systems, vol. 63, pp. 887-900, 2014.
240. G. Štimac, S. Braut, and R. Žigulić, "Comparative analysis of PSO algorithms for PID controller tuning," Chinese Journal of Mechanical Engineering, vol. 27, no. 5, pp. 928-936, 2014.
241. N. Nedic, D. Prsic, L. Dubonjic, V. Stojanovic, andV. Djordjevic, "Optimal cascade hydraulic control for a parallel robot platform by PSO," International Journal of Advanced Manufacturing Technology, vol. 72, no. 5-8, pp. 1085-1098, 2014.
242. W.-D. Chang and C.-Y. Chen, "PID controller design for MIMOprocesses using improved particle swarmoptimization," Circuits, Systems, and Signal Processing, vol. 33, no. 5, pp. 1473-1490, 2014.
243. X. Xiang, R. Mutlu, G. Alici, andW. Li, "Control of conducting polymer actuators without physical feedback: simulated feedback control approach with particle swarm optimization," SmartMaterials and Structures, vol. 23, no. 3, Article ID035014, 2014.
244. K. A. Danapalasingam, "Robust autonomous helicopter stabilizer tuned by particle swarm optimization," International Journal of Pattern Recognition and Artificial Intelligence, vol. 28, no. 1, Article ID 1459002, 2014.
245. M. J. Mahmoodabadi, M. Taherkhorsandi, and A. Bagheri, "Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO," Neurocomputing, vol. 124, pp. 194-209, 2014.
246. Y. Zhong, X. Huang, P. Meng, and F. Li, "PSO-RBF neural network PID control algorithm of electric gas pressure regulator," Abstract and Applied Analysis, vol. 2014, Article ID 731368, 7 pages, 2014.
247. J.-W. Perng, G.-Y. Chen, and S.-C. Hsieh, "Optimal pid controller design based on PSO-RBFNN for wind turbine systems," Energies, vol. 7, no. 1, pp. 191-209, 2014.
248. Y.-C. Huang and Y.-H. Li, "Experiments of iterative learning control system using particle swarm optimization by new bounded constraints on velocity and positioning," Engineering Computations, vol. 31, no. 2, pp. 250-266, 2014.
249. M. G. Nisha and G. N. Pillai, "Nonlinear model predictive control of MIMO system with relevance vector machines and particle swarm optimization," Control Engineering and Applied Informatics, vol. 16, no. 2, pp. 58-66, 2014.
250. M. Yousefi, M. Mosalanejad, G. Moradi, and A. Abdipour, "Dual band planar hybrid coupler with enhanced bandwidth using particle swarmoptimization technique," IEICE Electronics Express, vol. 9, no. 12, pp. 1030-1035, 2012.
251. X.-B. Sun, Z.-M. Li, C.-L. Zhao, and Z. Zhou, "Cognitive UWB pulse waveform design based on particle swarm optimization," Ad-Hoc & SensorWireless Networks, vol. 16, no. 1-3, pp. 215-227, 2012.
252. H. Yongqiang, L. Wentao, and L. Xiaohui, "Particle swarm optimization for antenna selection in MIMO system," Wireless Personal Communications, vol. 68, no. 3, pp. 1013-1029, 2013.
253. C.-C. Chiu, M.-H. Ho, and S.-H. Liao, "PSO and APSO for optimizing coverage in indoor UWB communication system," International Journal of RF and Microwave Computer-Aided Engineering, vol. 23, no. 3, pp. 300-308, 2013.
254. A. A. Minasian and T. S. Bird, "Particle swarm optimization of microstrip antennas for wireless communication systems," IEEE Transactions on Antennas and Propagation, vol. 61, no. 12, pp. 6214-6217, 2013.
255. M. Zubair and M.Moinuddin, "Joint optimization of microstrip patch antennas using particle swarm optimization for UWB systems," International Journal of Antennas and Propagation, vol. 2013, Article ID 649049, 8 pages, 2013.
256. Y. G. Kim and M. J. Lee, "Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization," IEEE Communications Magazine, vol. 52, no. 1, pp. 122-129, 2014.
257. A. Yazgan and I. Hakki Cavdar, "A comparative study between LMS and PSO algorithms on the optical channel estimation for radio over fiber systems," Optik, vol. 125, no. 11, pp. 2582-2586, 2014.
258. R. I. Rabady and A. Ababneh, "Global optimal design of optical multilayer thin-film filters using particle swarm optimization," Optik, vol. 125, no. 1, pp. 548-553, 2014.
259. G. Das, P. K. Pattnaik, and S. K. Padhy, "Artificial Neural Network trained by Particle SwarmOptimization for non-linear channel equalization," Expert Systems with Applications, vol. 41, no. 7, pp. 3491-3496, 2014.
260. S. Scott-Hayward and E. Garcia-Palacios, "Channel time allocation PSO for gigabit multimedia wireless networks," IEEE Transactions on Multimedia, vol. 16, no. 3, pp. 828-836, 2014.
261. A. Omidvar and K. Mohammadi, "Particle swarm optimization in intelligent routing of delay-tolerant network routing," EURASIP Journal onWireless Communications and Networking, vol. 2014, article 147, 8 pages, 2014.
262. P. Kuila and P. K. Jana, "Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach," Engineering Applications of Artificial Intelligence, vol. 33, pp. 127-140, 2014.
263. Z. H. Liu andX. L.Wang, "APSO-based algorithmfor load balancing in virtualmachines of cloud computing environment," in Advances in Swarm Intelligence:Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I, Y. Tan, Y. Shi, and Z. Ji, Eds., vol. 7331 of Lecture Notes in Computer Science, pp. 142-147, Springer, Berlin, Germany, 2012.
264. Z. H. Che, T.-A. Chiang, and Z.-G. Che, "Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationshipmanagement," Transactions of the Institute of Measurement and Control, vol. 34, no. 6, pp. 720-735, 2012.
265. V. Hajipour and S. H. R. Pasandideh, "Proposing an adaptive particle swarm optimization for a novel Bi-objective queuing facility location model," Economic Computation and Economic Cybernetics Studies and Research, vol. 46, no. 3, pp. 223-240, 2012.
266. F. J. Cabrerizo, E. Herrera-Viedma, andW. Pedrycz, "A method based on PSOand granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts," European Journal of Operational Research, vol. 230, no. 3, pp. 624-633, 2013.
267. K. Prescilla and A. I. Selvakumar, "Modified Binary particle swarm optimization algorithm application to real-time task assignment in heterogeneous multiprocessor," Microprocessors and Microsystems, vol. 37, no. 6-7, pp. 583-589, 2013.
268. H. B. Duan, X. X. Wei, and Z. N. Dong, "Multiple UCAVs cooperative air combat simulation platform based on PSO, ACO, and game theory," IEEE Aerospace and Electronic Systems Magazine, vol. 28, no. 11, pp. 12-19, 2013.
269. F. Belmecheri, C. Prins, F. Yalaoui, and L. Amodeo, "Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows," Journal of Intelligent Manufacturing, vol. 24, no. 4, pp. 775-789, 2013.
270. W. B. Hu,H. Liang,C.Peng, B. Du, andQ. Hu, "Ahybrid chaosparticle swarm optimization algorithm for the vehicle routing problem with time window," Entropy, vol. 15, no. 4, pp. 1247-1270, 2013.
271. A. Al Badawi and A. Shatnawi, "Static scheduling of directed acyclic data flow graphs onto multiprocessors using particle swarm optimization," Computers and Operations Research, vol. 40, no. 10, pp. 2322-2328, 2013.
272. F. P. Goksal, I.Karaoglan, and F. Altiparmak, "Ahybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery," Computers & Industrial Engineering, vol. 65, no. 1, pp. 39-53, 2013.
273. W. Zhang, H. Xie, B. Cao, and A.M. K. Cheng, "Energy-aware real-time task scheduling for heterogeneous multiprocessors with particle swarm optimization algorithm," Mathematical Problems in Engineering, vol. 2014, Article ID 287475, 9 pages, 2014.
274. P. N. Kechagiopoulos and G. N. Beligiannis, "Solving the urban transit routing problem using a particle swarm optimization based algorithm," Applied Soft Computing Journal, vol. 21, pp. 654-676, 2014.
275. L. Ming, H. Hai, Z. Aimin, S. Yingde, L. Zhao, and Z. Xingguo, "Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm," China Foundry, vol. 9, no. 2, pp. 119-124, 2012.
276. J. Q. Chen,Y.Tang, R. Ge,Q.An, andX.Guo, "Reliabilitydesign optimization of composite structures based on PSO together with FEA," Chinese Journal of Aeronautics, vol. 26, no. 2, pp. 343-349, 2013.
277. S. C. Mohan, D. K. Maiti, and D. Maity, "Structural damage assessment using FRF employing particle swarmoptimization," Applied Mathematics and Computation, vol. 219, no. 20, pp. 10387-10400, 2013.
278. J. Chen,Y.Tang, andX.Huang, "Application of surrogate based particle swarm optimization to the reliability-based robust design of composite pressure vessels," Acta Mechanica Solida Sinica, vol. 26, no. 5, pp. 480-490, 2013.
279. Y. L. Zhang, D. Gallipoli, and C. Augarde, "Parameter identification for elasto-plastic modelling of unsaturated soils from pressuremeter tests by parallel modified particle swarm optimization," Computers and Geotechnics, vol. 48, pp. 293-303, 2013.
280. G. H. Wang, J. Chen, T. Cai, and B. Xin, "Decompositionbased multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor," Engineering Optimization, vol. 45, no. 9, pp. 1107-1127, 2013.
281. T. Lazrag, M. Kacem, P. Dubujet, J. Sghaier, and A. Bellagi, "Determination of unsaturated hydraulic properties using drainage gravity test and particle swarm optimization algorithm," Journal of Porous Media, vol. 16, no. 11, pp. 1025-1034, 2013.
282. C.-H. Lee, K.-S. Shih, C.-C. Hsu, and T. Cho, "Simulationbased particle swarm optimization and mechanical validation of screw position and number for the fixation stability of a femoral locking compression plate," Medical Engineering & Physics, vol. 36, no. 1, pp. 57-64, 2014.
283. J. J. Lake, A. E. Duwel, and R. N. Candler, "Particle swarm optimization for design of slotted MEMS resonators with low thermoelastic dissipation," Journal of Microelectromechanical Systems, vol. 23, no. 2, pp. 364-371, 2014.
284. A. R. Vosoughi and S. Gerist, "New hybrid FE-PSO-CGAs sensitivity base technique for damage detection of laminated composite beams," Composite Structures, vol. 118, pp. 68-73, 2014.
285. S. L. M. Ribeiro, T. A. A. Silva, L. M. G. Vieira,T.H. Panzera,K. Boba, and F. Scarpa, "Geometric effects of sustainable auxetic structures integrating the particle swarm optimization and finite element method," Materials Research, vol. 17, no. 3, pp. 747-757, 2014.
286. P. Kitak, A. Glotic, and I. Ticar, "Heat transfer coefficients determination of numerical model by using particle swarm optimization," IEEE Transactions on Magnetics, vol. 50, no. 2, 2014.
287. R. Kalatehjari, A. S. A Rashid, N. Ali, and M. Hajihassani, "The contribution of particle swarm optimization to threedimensional slope stability analysis," The Scientific World Journal, vol. 2014,Article ID 973093, 12 pages, 2014.
288. B. Ashuri and M. Tavakolan, "Fuzzy enabled hybrid genetic algorithm-particle swarm optimization approach to solve TCRO problems in construction project planning," Journal of Construction Engineering and Management, vol. 138, no. 9, pp. 1065-1074, 2012.
289. A. Bozorgi-Amiri, M. S. Jabalameli, M. Alinaghian, and M. Heydari, "A modified particle swarm optimization for disaster relief logistics under uncertain environment," International Journal of Advanced Manufacturing Technology, vol. 60, no. 1-4, pp. 357-371, 2012.
290. J. Sadoghi Yazdi, F. Kalantary, and H. Sadoghi Yazdi, "Calibration of soil model parameters using particle swarm optimization," International Journal of Geomechanics, vol. 12, no. 3, pp. 229-238, 2012.
291. B. Bolat,O. Altun, andP. Cortés, "Aparticle swarmoptimization algorithm for optimal car-call allocation in elevator group control systems," Applied Soft Computing Journal, vol. 13, no. 5, pp. 2633-2642, 2013.
292. K. S. J. Babu and D. P. Vijayalakshmi, "Self-adaptive PSOGA hybrid model for combinatorial water distribution network design," Journal of Pipeline Systems Engineering and Practice, vol. 4, no. 1, pp. 57-67, 2013.
293. S. C. Mohan, A. Yadav, D. Kumar Maiti, and D. Maity, "A comparative study on crack identification of structures fromthe changes in natural frequencies using GA and PSO," Engineering Computations, vol. 31, no. 7, pp. 1514-1531, 2014.
294. M. Asadnia, L. H. C. Chua, X. S. Qin, and A. Talei, "Improved particle swarmoptimization-based artificial neural network for Rainfall-Runoff modeling," Journal of Hydrologic Engineering, vol. 19, no. 7, pp. 1320-1329, 2014.
295. B. Yan, S. Goto, A. Miyamoto, and H. Zhao, "Imaging-based rating for corrosion states of weathering steel using wavelet transform and PSO-SVM techniques," Journal of Computing in Civil Engineering, vol. 28, no. 3, Article ID04014008, 2014.
296. Y. Wang and L. Li, "A PSO algorithm for constrained redundancy allocation in multi-state systems with bridge topology," Computers & Industrial Engineering, vol. 68, no. 1, pp. 13-22, 2014.
297. J. Sadeghi, S. Sadeghi, and S. T. Niaki, "Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: an improved particle swarm optimization algorithm," Information Sciences, vol. 272, pp. 126-144, 2014.
298. Ž. Kanović, V. Bugarski, and T. Bačkalić, "Ship lock control system optimization using GA, PSO and ABC: a comparative review," Promet-Traffic & Transportation, vol. 26, no. 1, pp. 23-31, 2014.
299. P. Mandal, H. Zareipour, and W. D. Rosehart, "Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs," International Journal of Energy Research, vol. 38, no. 13, pp. 1654-1666, 2014.
300. K. H. Chao, "An extension theory-based maximum power tracker using a particle swarm optimization algorithm," Energy Conversion and Management, vol. 86, pp. 435-442, 2014.
301. G. G. Chen, L. Liu, P. Song, and Y. Du, "Chaotic improved PSObased multi-objective optimization for minimization of power losses and L index in power systems," Energy Conversion and Management, vol. 86, pp. 548-560, 2014.
302. C.Hu, G. Jain, P. Zhang, C. Schmidt, P.Gomadam, andT.Gorka, "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, vol. 129, pp. 49-55, 2014.
303. I. Tabet, K. Touafek, N. Bellel, N. Bouarroudj, A. Khelifa, and M. Adouane, "Optimization of angle of inclination of the hybrid photovoltaic-thermal solar collector using particle swarm optimization algorithm," Journal of Renewable and Sustainable Energy, vol. 6, no. 5,Article ID 053116, 2014.
304. S. Bahrami, R. A. Hooshmand, andM. Parastegari, "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, vol. 72, pp. 434-442, 2014.
305. A. Askarzadeh, "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: a case study of Iran," Energy, vol. 72, pp. 484-491, 2014.
306. M. Sharafi and T. Y. ElMekkawy, "Multi-objective optimal design of hybrid renewable energy systems using PSOsimulation based approach," Renewable Energy, vol. 68, pp. 67-79, 2014.
307. P. García-Triviño, F. Llorens-Iborra, C. A. García-Vázquez, A. J. Gil-Mena, L. M. Fernández-Ramírez, and F. Jurado, "Longtermoptimization based on PSO of a grid-connected renewable energy/battery/hydrogen hybrid system," International Journal of Hydrogen Energy, vol. 39, no. 21, pp. 10805-10816, 2014.
308. S. Biao, H. Chang hua, Y. Xin hua, and H. Chuan, "Mutation particle swarm optimization algorithm for solving the optimal operationmodel of thermal power plants," Journal of Renewable and Sustainable Energy, vol. 6, no. 4,Article ID 043118, 2014.
309. J.-H. Chen, H.-T. Yau, and W. Hung, "Design and study on slidingmode extremumseeking control of the chaos embedded particle swarm optimization for maximum power point tracking inwind power systems," Energies, vol. 7, no. 3,pp. 1706-1720, 2014.
310. M. Seyedmahmoudian, S. Mekhilef, R. Rahmani, R. Yusof, and A. Asghar Shojaei, "Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique," Journal of Renewable and Sustainable Energy, vol. 6, no. 2,Article ID 023102, 2014.
311. M. M. Aman, G. B. Jasmon, A. H. A. Bakar, and H.Mokhlis, "A new approach for optimum simultaneousmulti-DGdistributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, vol. 66, pp. 202-215, 2014.
312. C.-L. Xiao and H.-X. Huang, "Optimal design of heating system for rapid thermal cycling mold using particle swarm optimization and finite element method," Applied Thermal Engineering, vol. 64, no. 1-2, pp. 462-470, 2014.
313. K. L. Lian, J.H. Jhang, and I. S. Tian, "Amaximumpower point tracking method based on perturb-and-observe combined with particle swarmoptimization," IEEE Journal of Photovoltaics, vol. 4, no. 2, pp. 626-633, 2014.
314. S. N. Qasem and S. M. Shamsuddin, "Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis," Applied Soft Computing Journal, vol. 11, no. 1, pp. 1427-1438, 2011.
315. Y. Zhang, S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO," Progress in Electromagnetics Research, vol. 109, pp. 325-343, 2010.
316. X. D. Guo, C. Wang, and R. G. Yan, "An electromagnetic localization method for medical micro-devices based on adaptive particle swarm optimization with neighborhood search," Measurement, vol. 44, no. 5, pp. 852-858, 2011.
317. P.-C.Chang, J.-J. Lin, andC.-H. Liu, "An attributeweight assignment and particle swarm optimization algorithm for medical database classifications," Computer Methods and Programs in Biomedicine, vol. 107, no. 3, pp. 382-392, 2012.
318. L.-F. Chen, C.-T. Su, K.-H. Chen, and P.-C. Wang, "Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis," Neural Computing & Applications, vol. 21, no. 8, pp. 2087-2096, 2012.
319. W.-T. Sung and Y.-C. Chiang, "Improved particle swarm optimization algorithm for android medical care iot using modified parameters," Journal ofMedical Systems, vol. 36, no. 6, pp. 3755-3763, 2012.
320. I. Cruz-Aceves, J. G. Aviña-Cervantes, J. M. López-Hernández, and S. E. González-Reyna, "Multiple active contours driven by particle swarm optimization for cardiac medical image segmentation," Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 132953, 13 pages, 2013.
321. A. Subasi, "Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders," Computers in Biology and Medicine, vol. 43, no. 5, pp. 576-586, 2013.
322. Y. Zhang, S. Wang, G. Ji, and Z. Dong, "An MR brain images classifier system via particle swarm optimization and kernel support vectormachine," The ScientificWorld Journal, vol. 2013, Article ID 130134, 9 pages, 2013.
323. V. Mangat and R. Vig, "Novel associative classifier based on dynamic adaptive PSO: application to determining candidates for thoracic surgery," Expert Systems with Applications, vol. 41, no. 18, pp. 8234-8244, 2014.
324. D. Mandal, A. Chatterjee, and M. Maitra, "Robust medical image segmentation using particle swarm optimization aided level set based global fitting energy active contour approach," Engineering Applications of Artificial Intelligence, vol. 35, pp. 199-214, 2014.
325. Y.-Z. Hsieh, M.-C. Su, and P.-C. Wang, "A PSO-based rule extractor for medical diagnosis," Journal of Biomedical Informatics, vol. 49, pp. 53-60, 2014.
326. S. Ganapathy, R. Sethukkarasi,P.Yogesh, P. Vijayakumar, andA. Kannan, "An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization," Academy Proceedings in Engineering Sciences, vol. 39, no. 2, pp. 283-302, 2014.
327. P. Thakral, V. Arora, S. Kukreti, and A. K. Bakhshi, "Insilico engineering of intrinsically conducting copolymers using particle swarm optimization algorithm," Indian Journal of Chemistry-Section A Inorganic, Physical,Theoretical and Analytical Chemistry, vol. 52, no. 3, pp. 317-326, 2013.
328. H. Parastar, H. Ebrahimi-Najafabadi, and M. Jalali-Heravi, "Multivariate curve resolution-particle swarm optimization: a high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals," Analytica Chimica Acta, vol. 772, pp. 16-25, 2013.
329. M. Khajeh, M. Kaykhaii, and A. Sharafi, "Application of PSOartificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples," Journal of Industrial and Engineering Chemistry, vol. 19, no. 5, pp. 1624-1630, 2013.
330. Y.Wu, B. Liu,M. Li, and K. Tang, "Prediction of CO2 solubility in polymers by radial basis function artificial neural network based on chaotic self-adaptive particle swarmoptimization and fuzzy clustering method," Chinese Journal of Chemistry, vol. 31, no. 12, pp. 1564-1572, 2013.
331. M. Khajeh and K. Dastafkan, "Removal of molybdenum using silver nanoparticles from water samples: particle swarm optimization-artificial neural network," Journal of Industrial and Engineering Chemistry, vol. 20, no. 5, pp. 3014-3018, 2014.
332. P.Thakral, V. Kapoor, and A. K. Bakhshi, "In silico architecturing of novel hetero-aromatic bicyclic copolymers using particle swarm optimization algorithm," Indian Journal of Chemistry Section A: Inorganic, Bio-Inorganic, Physical, Theoretical & Analytical Chemistry, vol. 53, no. 1, pp. 9-16, 2014.
333. S. Y. Sun and J. W. Li, "Parameter estimation of methanol transformation into olefins through improved particle swarm optimization with attenuation function," Chemical Engineering Research & Design, vol. 92, no. 11, pp. 2083-2094, 2014.
334. A.N. Skvortsov, "Estimation of rotation ambiguity inmultivariate curve resolution with charged particle swarm optimization (cPSO-MCR)," Journal of Chemometrics, vol. 28,no. 10, pp. 727-739, 2014.
335. M. A. Khansary and A. H. Sani, "Using genetic algorithm (GA) and particle swarm optimization (PSO) methods for determination of interaction parameters in multicomponent systems of liquid-liquid equilibria," Fluid Phase Equilibria, vol. 365, pp. 141-145, 2014.
336. R. Nasimi and R. Irani, "Identification and modeling of a yeast fermentation bioreactor using hybrid particle swarm optimization-artificial neural networks," Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 36, no. 14, pp. 1604-1611, 2014.
337. S. Saraswathi, S. Sundaram,N. Sundararajan, M. Zimmermann, andM.Nilsen-Hamilton, "ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented," IEEE/ACMTransactions on Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 452-463, 2011.
338. N.Mansour, F. Kanj, andH. Khachfe, "Particle swarmoptimization approach for protein structure prediction in the 3D HP model," Interdisciplinary Sciences: Computational Life Sciences, vol. 4, no. 3, pp. 190-200, 2012.
339. M. Karabulut and T. Ibrikci, "A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to identify transcription factor binding sites," Applied Soft Computing Journal, vol. 12, no. 9, pp. 2846-2855, 2012.
340. Y. J. Liu, X. Shi, G. Huang, B. Li, and L. Zhao, "Classification of diffuse large B cell lymphoma gene expression data based on two-layer particle swarmoptimization," in Proceedings of the 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '13), pp. 422-427, Shenyang, China, July 2013.
341. M. Salahi, A. Jamalian, and A. Taati, "Global minimization of multi-funnel functions using particle swarm optimization," Neural Computing and Applications, vol. 23, no. 7-8, pp. 2101-2106, 2013.
342. Z. Du, Y. Zhu, andW. Liu, "Combining quantum-behaved PSO and K2 algorithm for enhancing gene network construction," Current Bioinformatics, vol. 8, no. 1, pp. 133-137, 2013.
343. L.-Y. Chuang, H.-Y. Lane, Y.-D. Lin, M.-T. Lin, C.-H. Yang, and H.-W. Chang, "Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm," Annals of General Psychiatry, vol. 13, article 15, 2014.
344. M.Mandal and A.Mukhopadhyay, "Agraph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO," PLoS ONE, vol. 9, no. 3, Article ID e90949, 2014.
345. K.-H. Chen, K.-J. Wang, M.-L. Tsai et al., "Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm," BMC Bioinformatics, vol. 15, no. 1, article 49, 2014.
346. P. J. García Nieto, E. García-Gonzalo, J. R. Alonso Fernández, and C.DíazMuñiz, "Hybrid PSO-SVM-based method for longterm forecasting of turbidity in the Nalón river basin: a case study in Northern Spain," Ecological Engineering, vol. 73, pp. 192-200, 2014.
347. J. T. Mentzer, W. DeWitt, J. S. Keebler et al., "Defining supply chain management," Journal of Business Logistics, vol. 22, no. 2, pp. 1-25, 2001.
348. L. Altamirano, M. C. Riff, I. Araya, and L. Trilling, "Anesthesiology nurse scheduling using particle swarm optimization," International Journal of Computational Intelligence Systems, vol. 5, no. 1, pp. 111-125, 2012.
349. Z. G. Zeng, J.Xu, S.Wu, andM. Shen, "Antitheticmethod-based particle swarm optimization for a queuing network problem with fuzzy data in concrete transportation systems," Computer-Aided Civil and Infrastructure Engineering, vol. 29, no. 10, pp. 771-800, 2014.